Handling and Interpreting Missing Modalities in Patient Clinical Trajectories via Autoregressive Sequence Modeling
Summary: arXiv:2604.18753v1 Announce Type: cross
Introduction
In the realm of healthcare, the integration of machine learning (ML) techniques has revolutionized the way clinical data is analyzed and interpreted. However, one of the persistent challenges in developing effective multimodal ML models for healthcare is managing the issue of missing modalities. Clinical datasets are often temporal and exhibit sparse modality presence, making it difficult to capture the underlying predictive signals essential for accurate diagnostics. This article explores a novel approach to addressing these challenges by reframing clinical diagnosis as an autoregressive sequence modeling task.
Challenges in Multimodal Machine Learning
Handling missing modalities during both training and deployment phases poses significant hurdles. Some of the primary challenges include:
- Data Sparsity: Clinical datasets frequently have gaps in modality presence, leading to incomplete information.
- Model Explainability: Ensuring that ML models can provide clear and understandable insights into their decision-making processes remains critical, especially in healthcare.
- Predictive Accuracy: Maintaining high predictive accuracy in the presence of missing data is essential for effective clinical decision-making.
Proposed Methodology
The authors propose a novel approach that leverages causal decoders from large language models (LLMs) to effectively model a patient’s multimodal trajectory. The methodology comprises several key components:
- Missingness-Aware Contrastive Pre-training: This innovative objective integrates multiple modalities in datasets, accounting for missingness in a shared latent space, which enables the model to learn more robust representations.
- Autoregressive Sequence Modeling: By employing transformer-based architectures, the proposed method demonstrates superior performance compared to existing baselines on fine-tuning benchmarks such as MIMIC-IV and eICU.
Performance Evaluation
The results of the experiments reveal that autoregressive sequence modeling significantly outperforms traditional methods in handling missing modalities. Notably, the use of interpretability techniques further enhances the understanding of model behavior. The findings indicate:
- Across various patient stays, the removal of modalities resulted in divergent model behavior, which the contrastive pre-training effectively mitigated.
- The framework allows for better profiling of patient trajectories, providing insights into the implications of missing data on model performance.
Conclusion
This research contributes to the ongoing discourse surrounding safe and transparent clinical AI by offering a framework that not only addresses the technical challenges of missing modalities but also prioritizes model interpretability. By abstracting clinical diagnosis as sequence modeling, this approach enhances the ability to manage and interpret complex patient data while ensuring that the healthcare system can leverage advanced ML techniques responsibly.